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F. M. Smith and G. O. Roberts, “Bayesian Computation via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods,” Journal of the Royal Statistical Society, Series B, Vol. 55, No. 1, 1993, pp. 3-24.

has been cited by the following article:

• TITLE: Parallel Computing with a Bayesian Item Response Model

  AUTHORS: Kyriakos Patsias, Mona Rahimi, Yanyan Sheng, Shahram Rahimi

  KEYWORDS: Gibbs Sampling; High Performance Computing; Message Passing Interface; Two-Parameter IRT Model

  JOURNAL NAME: American Journal of Computational Mathematics, Vol.2 No.2, June 21, 2012

  ABSTRACT: Item response theory (IRT) is a modern test theory that has been used in various aspects of educational and psychological measurement. The fully Bayesian approach shows promise for estimating IRT models. Given that it is computation- ally expensive, the procedure is limited in practical applications. It is hence important to seek ways to reduce the execution time. A suitable solution is the use of high performance computing. This study focuses on the fully Bayesian algorithm for a conventional IRT model so that it can be implemented on a high performance parallel machine. Empirical results suggest that this parallel version of the algorithm achieves a considerable speedup and thus reduces the execution time considerably.